1. ES Design, Development and Operation Dr. Ahmed Elfaig Knowledge model, knowledge structure, presentation and organization are the bottleneck of expert system development Knowledge model can be graphically illustrated to reflect the component and integrated nature of different modules of the problem domain. The conceptual model of the problem and the problem sub-module are shown in the figure below:

2. Problem Domain and Methods of Assessment

3. Example of General Knowledge base of ESCNP: Residential Area

7. Example of General Knowledge base of ESCNP: School Compound

9. Example of General Knowledge base of ESCNP: Hospital Area

12. ES Development Phases

13. The testing phase aims at showing, validating and verifying the model and software of ES functions. It shows the overall structure of the system and its knowledge (verification shows no bugs or technical errors) Traces syntax errors that may prevent the rules from firing and fixing such errors

15. Goals of Verification Make sure there are no: Bug Technical errors Removing errors Incompleteness Ambiguity Inconsistency in system function

16. Knowledge Acquisition Knowledge acquisition : Is processes involve collecting, eliciting, organizing, analyzing and interpreting the knowledge that human experts use when solving particular problem Knowledge acquisition involve includes knowledge refinement, validation and verification.

17. Importance of Knowledge acquisition Importance of knowledge come from the fact that : The power utility of any system depends on underlying knowledge quality The clients acceptance of the system depends on the validity of the knowledge it has.

18. Type of knowledge Declarative knowledge: which is used to describe the problem characteristics and concepts Heuristic knowledge: Knowledge used to make judgement or strategic rule of thumb.

19. VALIDATION Comparison of research output (knowledge) with the heuristic of expert in the field Comparison of the research output with known results

20. TYPE OF VALIDITY Content validity Criterion validity Objective validity Subjective validity

21. Content Validity Results of the system or research test against experts The system models test against other models

22. Criterion validity Level of expertise provided by the research or a system

23. OBJECTIVE VALIDITY Actual system Performance Actual outcome

24. SUBJECTIVE VALIDITY Research results or system performance compare to experts.

25. VALIDATION PROCESSES Known results: for example WHO Blind performance test: Compare the results against human experts Face validation: Qualitative procedure to test the results Subjective evaluation: Evaluation of the results through consultation with experts

26. Validation: Assessments Results STDMeanParameters considered 0.02 0.01 3.84 3.72 Variable: 1.Completeness 2.Importance 0.043.96 Output: 1. Important results 0.03 0.02 3.88 3.8 Performance: 1.Right results 2. Complete results 0.033.4Explanation; 1.Why certain variables are needed

27. Field Testing % complianceNumerical differences Research or system output Observed Results -0.015-0.961.460.5.104.94448.9